The evolution of a generalized neural learning rule

Evolution is extremely creative. The mere availability of a mechanism for synaptic change seems to be enough for evolution to derive a learning rule. Many simulations of evolution have evolved learning in a highly guided manner. Either by constraining the update function to a Hebbian form, or by supplying an error/teaching signal. In this paper, we aim to evolve a more general learning rule. And since neural networks are so versatile, we construct the learning function itself out of a neural network. Our evolved networks excel at the foraging task they evolved in. Amazingly, they even function robustly when tested outside of their historical niche. The same cannot be said for the Hebbian learning networks we compare to.

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  David J. Chalmers,et al.  The Evolution of Learning: An Experiment in Genetic Connectionism , 1991 .

[3]  Hod Lipson,et al.  Distilling Free-Form Natural Laws from Experimental Data , 2009, Science.

[4]  Risto Miikkulainen,et al.  Evolving adaptive neural networks with and without adaptive synapses , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[5]  Magnus Thor Jonsson,et al.  Evolution and design of distributed learning rules , 2000, 2000 IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks. Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (Cat. No.00.

[6]  Sebastian Risi,et al.  Indirectly Encoding Neural Plasticity as a Pattern of Local Rules , 2010, SAB.

[7]  Dario Floreano,et al.  Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios , 2008, ALIFE.

[8]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[9]  Jeffrey L. Elman,et al.  Learning and Evolution in Neural Networks , 1994, Adapt. Behav..

[10]  Alan D. Blair,et al.  Evolving Plastic Neural Networks for Online Learning: Review and Future Directions , 2012, Australasian Conference on Artificial Intelligence.

[11]  Dario Floreano,et al.  Evolutionary robots with on-line self-organization and behavioral fitness , 2000, Neural Networks.

[12]  Y. Niv,et al.  Evolution of Reinforcement Learning in Uncertain Environments: A Simple Explanation for Complex Foraging Behaviors , 2002 .

[13]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[14]  Geoffrey E. Hinton,et al.  How Learning Can Guide Evolution , 1996, Complex Syst..

[15]  Jean-Baptiste Mouret,et al.  On the relationships between synaptic plasticity and generative systems , 2011, GECCO '11.